OPLS methods for the analysis of hyperspectral images—comparison with MCR-ALS
2014 (English)In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 28, no 8, 687-696 p.Article in journal (Refereed) Published
Two new orthogonal projections to latent structures (OPLS) based methods were proposed to analyze hyperspectral images, enabling the visualization ofmultiple chemical compounds in onematrix without the need of extensive preprocessing. Both proposed methods delivered images representing the chemical distribution in the ribbon similar to the more traditional multivariate curve resolution–alternating least squares (MCR-ALS) method, but their image background was less dynamic resulting in a stronger chemical contrast. This indicated that the methods successfully removed structured variation orthogonal to the chemical information (pure spectra of individual compounds), which was confirmed by the fact that physical scattering effects caused by grooves and edges were captured in the images visualizing the orthogonal components of the model. Hereby, the OPLS-based method employing the pure spectra as weights in the OPLS algorithm was more successful in distinguishing compounds with a similar spectral signal than the transposed OPLS algorithm(pure spectra of individual compounds were used as response in OPLS model). It should be noted that for the main compounds, the MCR-ALS method enabled easier visual interpretation compared to the OPLS-based methods by setting all values below zero to zero, resulting in a higher contrast between pixels containing the studied compound and pixels not containing that compound.
Place, publisher, year, edition, pages
John Wiley & Sons, 2014. Vol. 28, no 8, 687-696 p.
hyperspectral imaging, orthogonal projections to latent structures, multivariate curve resolution–alternating least squares
Research subject Computerized Image Analysis; Computer Science; Pharmaceutics
IdentifiersURN: urn:nbn:se:umu:diva-89128DOI: 10.1002/cem.2628ISI: 000340504100014OAI: oai:DiVA.org:umu-89128DiVA: diva2:718779
ProjectsInnovative Multivariate Model Based Approaches For Industry